diff --git a/3_visualize/src/python_scripts/plot_pred_performance.py b/3_visualize/src/python_scripts/plot_pred_performance.py
index fbacc137..5806da53 100644
--- a/3_visualize/src/python_scripts/plot_pred_performance.py
+++ b/3_visualize/src/python_scripts/plot_pred_performance.py
@@ -19,45 +19,20 @@
 validation_sites = ["01472104", "01473500", "01481500"]
 test_sites = ["01475530", "01475548"]
 
-models = ["0_baseline_LSTM", "2_multitask_dense"]
 
-
-def read_and_combine_dfs(model_ids, metric_type, partition, parent_dir):
-    f_name = "{p}{model}/exp_{metric_type}_metrics.csv"
-    dfs = []
-    for m in model_ids:
-        df = pd.read_csv(f_name.format(p=parent_dir, model=m, metric_type=metric_type), dtype={'site_id':str})
-        df['model'] = m
-        dfs.append(df)
-
-    df_comb = pd.concat(dfs)
+def read_and_filter_df(metric_type, partition):
+    f_name = f"../../../2a_model/out/models/combined_{metric_type}_metrics.csv"
+    df_comb = pd.read_csv(f_name, dtype={"site_id": str})
     df_comb = df_comb[df_comb['partition'] == partition]
     df_comb = df_comb[df_comb['variable'].str.startswith('do')]
     df_comb = df_comb[df_comb['rmse'].notna()]
     return df_comb
 
 
-df_comb_reach_new = read_and_combine_dfs(models, 'reach', 'val', "../../")
-df_comb_reach_new['type'] = 'new inputs'
-
-df_comb_reach_old = read_and_combine_dfs(models, "reach", 'val', "archive_221215/")
-df_comb_reach_old['type'] = 'old inputs'
-
-df_comb_reach = pd.concat([df_comb_reach_new, df_comb_reach_old])
-
-# +
-g = sns.catplot(x='site_id', y='rmse', row='model', col='variable', data=df_comb_reach, hue='type', kind='bar', legend=False, ci='sd')
-g.set_xticklabels(rotation=90)
-for ax in g.axes.flatten():
-    ax.grid()
-    ax.set_axisbelow(True)
-
-plt.legend(bbox_to_anchor=(1.05, .55))
-plt.tight_layout()
-plt.savefig("figs/val_results_new_inputs.png")
+df_comb_reach = read_and_filter_df("reach", "val")
 
 # +
-g = sns.catplot(x='site_id', y='rmse', col='variable', data=df_comb_reach_new, hue='model', kind='bar', legend=False, ci='sd')
+g = sns.catplot(x='site_id', y='rmse', col='variable', data=df_comb_reach, hue='model_id', kind='bar', legend=False, ci='sd')
 g.set_xticklabels(rotation=90)
 for ax in g.axes.flatten():
     ax.grid()
@@ -65,53 +40,35 @@ def read_and_combine_dfs(model_ids, metric_type, partition, parent_dir):
 
 plt.legend(bbox_to_anchor=(1.05, .55))
 plt.tight_layout()
-plt.savefig("figs/val_results_new_inputs.png")
+plt.savefig("../../out/pred_perf/val_results_by_site.png")
+plt.clf()
 # -
 
-g=sns.catplot(x='site_id', y='rmse', hue='model', col='variable', col_wrap=3, data=df_comb_reach, dodge=True, legend=False)
+g=sns.catplot(x='site_id', y='rmse', hue='model_id', col='variable', col_wrap=3, data=df_comb_reach, dodge=True, legend=False)
 g.set_xticklabels(rotation=90)
 for ax in g.axes.flatten():
     ax.grid()
-plt.legend(bbox_to_anchor=(1.05, .55))
-plt.tight_layout()
-plt.savefig('val_results_strip.png')
 
-# +
-g=sns.catplot(x='variable', y='rmse', hue='model', col='site_id', col_wrap=3, data=df_comb_reach, dodge=True, legend=False)
 for site_id, ax in g.axes_dict.items():
     ax.grid()
     if site_id in validation_sites:
         ax.text(1, 3.2, "**Validation Site**", ha='center')
-
-    
-plt.legend(bbox_to_anchor=(1.5, 1.15))
+plt.legend(bbox_to_anchor=(1.05, .55))
 plt.tight_layout()
-plt.savefig('val_results_strip.png')
-# -
-
-df_comb = read_and_combine_dfs(models, 'overall', 'val', "archive_221215/")
-
-df_comb_new = read_and_combine_dfs(models, 'overall', 'val', "./")
-df_comb_new['type'] = 'new inputs'
+plt.savefig('../../out/pred_perf/val_results_by_site_strip.png')
+plt.clf()
 
-df_comb_old = read_and_combine_dfs(models, "overall", 'val', "archive_221215/")
-df_comb_old['type'] = 'old inputs'
-
-df_comb = pd.concat([df_comb_new, df_comb_old])
+# -
 
-g = sns.catplot(x='variable', y='rmse', data=df_comb, hue='type', col='model', kind="bar")
-for ax in g.axes.flatten():
-    ax.bar_label(ax.containers[0], label_type="center")
-    ax.bar_label(ax.containers[1], label_type="center")
-plt.savefig("figs/val_results_overall_new_inputs.png")
+df_comb = read_and_filter_df('overall', 'val')
 
 # +
-ax = sns.barplot(x='variable', y='rmse', data=df_comb_new, hue='model')
-ax.bar_label(ax.containers[0], label_type="center")
-ax.bar_label(ax.containers[1], label_type="center")
+fig, ax = plt.subplots(figsize=(6,4))
+ax = sns.barplot(x='variable', y='rmse', data=df_comb, hue='model_id', ax=ax)
+ax.bar_label(ax.containers[0], label_type="center", fmt='%.2f')
+ax.bar_label(ax.containers[1], label_type="center", fmt='%.2f')
 
-# plt.tight_layout()
-plt.savefig("figs/val_results_overall.png")
+plt.savefig("../../out/pred_perf/val_results_overall.png")
 # -